Felix Yanwei Wang

I am an EECS PhD student at MIT CSAIL, working with Julie Shah on learning from human-AI interaction.

Before MIT, I did my MS in robotics at Northwestern University and researched with Todd Murphey and Mitra Hartmann on active sensing. I completed my undergraduate degree in physics at Middlebury College with Richard Wolfson.

Outside research, I enjoy theatre and backpacking. I thru-hiked PCT in 2019.

Email / Twitter / Google Scholar / LinkedIn / CV

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  • [Mar 2024] MIT News coverage of our ICLR 2024 paper on grounding language plans in demonstrations.
  • [Mar 2024] Invited talk at Brown University Robotics Seminar.
  • [Mar 2024] Invited talk at University of Utah.
  • [Jan 2024] Check out our Generative AI newsletter from MIT's work of the future group.
  • [Oct 2023] Temporal logic imitation was awarded the Best Student Paper at IROS 2023 Workshop.
  • [Sep 2023] Selected as Work of the Future Fellow in Generative AI.
  • [Jan 2023] PBS News coverage of our human robot interaction exhibition at MIT museum.


I'm interested in human robot (or any AI system such as LLM) interaction--for example, how we can leverage past interaction data to personalize and robustify future robot executions under adversarial perturbations. My PhD work applies continuous motion imitation to long-horizon manipulation tasks with discrete structures, aka interactive task and motion imitation.

Grounding Language Plans in Demonstrations through Counter-factual Perturbations
Yanwei Wang, Tsun-Hsuan Wang, Jiayuan Mao, Michael Hagenow, Julie Shah
arxiv / code (coming soon) / project page
ICLR 2024 (Spotlight, acceptance rate: 5%)

This work learns grounding classifiers for LLM planning. By locally perturbing a few human demonstrations, we augment the dataset with more successful executions and failing counterfactuals. Our end-to-end explanation-based network is trained to differentiate successes from failures and as a by-product learns classifiers that ground continuous states into discrete manipulation mode families without dense labeling.

Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrations
Yanwei Wang, Nadia Figueroa, Shen Li, Ankit Shah, Julie Shah
arxiv / code / project page
CoRL 2022 (Oral, acceptance rate: 6.5%)
IROS 2023 Workshop ( Best Student Paper, Learning Meets Model-based Methods for Manipulation and Grasping Workshop)

We present a continuous motion imitation method that can provably satisfy any discrete plan specified by a Linear Temporal Logic (LTL) formula. Consequently, the imitator is robust to both task- and motion-level disturbances and guaranteed to achieve task success.

Improving Small Language Models on PubMedQA via Generative Data Augmentation
Zhen Guo, Yanwei Wang, Peiqi Wang, Shangdi Yu
KDD 2023 (Foundations and Applications in Large-scale AI Models Pre-training, Fine-tuning, and Prompt-based Learning Workshop)

We prompt large language models to augment a domain-specific dataset to train specialized small language models that outperform the general-purpose LLM.

Visual Pre-training for Navigation: What Can We Learn from Noise?
Yanwei Wang, Ching-Yun Ko, Pulkit Agrawal
arxiv / code / project page
IROS 2023
NeurIPS 2022 Workshop (Synthetic Data for Empowering ML Research / Self-Supervised Learning)

By learning how to pan, tilt and zoom its camera to focus on random crops of a noise image, an embodied agent can pick up navigation skills in realistically simulated environments.

MIT Museum Interactive Robot Exhibition: Teach a Robot Motions
Nadia Figueroa, Yanwei Wang, Julie Shah

We installed an interactive exhibition at MIT Museum that allows non-robot-experts to teach a robot an inspection task using demonstrations. The robustness and compliance of the learned motion policy enables visitors (including kids) to physically perturb the system safely 24/7 without losing a success gaurantee.

Active Learning for Object Search
Yanwei Wang, Todd Murphey

This research project studies the application of two information gain methods in a search problem: 1. Infotaxis - Search guided by entropy minimization 2. Ergodic exploration - Search guided by proportional coverage.

Active Sensing with Tactile Sensors
Yanwei Wang, Mitra Hartmann

This research project studies how active sensing, i.e., choosing what data to collect, can improve data efficiency for decision-making under uncertainty. Inspired by the active whisking behavior of rats, we use simulated rat whisker sensory signals as a model for spatial-temporal data to learn policies that first collect observations and then classify object shapes.

Multi-agent Distributed Sensing and Control
Yanwei Wang, Michael Rubinstein

This research project studies multi-agent distributed algorithms concerning coordination, segregation, and locomotion, with a hardware implementation of robust localization with cheap sensors on a low-cost underactuated system.

Analyzing Energy Efficiency of Bio-Inspired Wind Generator
Yanwei Wang, Richard Wolfson


For my undergraduate physics thesis at Middlebury College, I did a computational fluid dynamics (CFD) simulation of Festo's Dual-Wing generator using COMSOL CFD package.


Miscellaneous class projects.

Planning through Soft Contact
Terry Suh, Yanwei Wang


We compare the effectiveness of relaxation methods for warm starting trajectory optimization through soft contact.

Lane Detection for Self-Driving Applications